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Dimensionality reduction, also known as manifold learning, is an area of machine learning used for extracting informative features from data for better representation of data or separation between classes. This book presents a cohesive review of linear and nonlinear dimensionality reduction and manifold learning. Three main aspects of dimensionality reduction are covered: spectral dimensionality reduction, probabilistic dimensionality reduction, and neural network-based dimensionality reduction, which have geometric, probabilistic, and information-theoretic points of view to dimensionality reduction, respectively. The necessary background and preliminaries on linear algebra, optimization, and kernels are also explained to ensure a comprehensive understanding of the algorithms. The tools introduced in this book can be applied to various applications involving feature extraction, image processing, computer vision, and signal processing. This book is applicable to a wide audience who would like to acquire a deep understanding of the various ways to extract, transform, and understand the structure of data. The intended audiences are academics, students, and industry professionals. Academic researchers and students can use this book as a textbook for machine learning and dimensionality reduction. Data scientists, machine learning scientists, computer vision scientists, and computer scientists can use this book as a reference. It can also be helpful to statisticians in the field of statistical learning and applied mathematicians in the fields of manifolds and subspace analysis. Industry professionals, including applied engineers, data engineers, and engineers in various fields of science dealing with machine learning, can use this as a guidebook for feature extraction from their data, as the raw data in industry often require preprocessing. The book is grounded in theory but provides thorough explanations and diverse examples to improve the reader’s comprehension of the advanced topics. Advanced methods are explained in a step-by-step manner so that readers of all levels can follow the reasoning and come to a deep understanding of the concepts. This book does not assume advanced theoretical background in machine learning and provides necessary background, although an undergraduate-level background in linear algebra and calculus is recommended.

Dimension reduction (Statistics) --- Machine learning. --- Learning, Machine --- Artificial intelligence --- Machine theory --- Dimensionality reduction (Statistics) --- Reduction, Dimension (Statistics) --- Reduction, Dimensionality (Statistics) --- Statistics

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Biosensors are analytical devices used for the detection of a chemical substance, or analyte, which combines a biological component with a physicochemical detector. Detection and quantification are based on the measurement of the biological interactions. The biological element of a biosensor may consist of tissues, microorganisms, organelles, cell receptors, enzymes, antibodies and nucleic acids. These devices have been shown to have a wide range of applications in a vast array of fields of research, including environmental monitoring, food analysis, drug detection and health and clinical assessment, and even security and safety. The current Special Issue, “Biosensors: 10th Anniversary Feature Papers”, addresses the existing knowledge gaps and aids the advancement of biosensing applications, in the form of six peer-reviewed research and review papers, detailing the most recent and innovative developments of biosensors.

Technology: general issues --- History of engineering & technology --- Materials science --- antioxidants --- biosensor --- nanomaterials --- vasodilator activity --- breast cancer screening --- imaging biomarker --- deep sparse autoencoder --- dimensionality reduction --- deep-learning features --- Bioassays --- high performance thin layer chromatography --- endocrine disrupting compounds --- fluorescent proteins --- wastewater --- electromagnetic piezoelectric acoustic sensor --- quartz --- adsorption --- diagnostics --- extracellular vesicle --- dithiocarbamate fungicides --- chromatography --- Raman spectroscopy --- sensors --- enzyme inhibition --- voltammetry --- biosensors --- tobacco mosaic virus (TMV) --- capacitive field-effect sensor --- bi-enzyme biosensor --- enzyme-logic gate --- urease --- penicillinase

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This book is published open access under a CC BY 4.0 license. It covers aspects of unsupervised machine learning used for knowledge discovery in data science and introduces a data-driven approach to cluster analysis, the Databionic swarm(DBS). DBS consists of the 3D landscape visualization and clustering of data. The 3D landscape enables 3D printing of high-dimensional data structures.The clustering and number of clusters or an absence of cluster structure are verified by the 3D landscape at a glance. DBS is the first swarm-based technique that shows emergent properties while exploiting concepts of swarm intelligence, self-organization and the Nash equilibrium concept from game theory. It results in the elimination of a global objective function and the setting of parameters. By downloading the R package DBS can be applied to data drawn from diverse research fields and used even by non-professionals in the field of data mining. Contents Approaches to Unsupervised Machine Learning Methods of Visualization of High-Dimensional Data Quality Assessments of Visualizations Behavior-Based Systems in Data Science Databionic Swarm (DBS) Target Groups Lecturers, students as well as non-professional users of data science, statistics, computer science, business mathematics, medicine, biology The Author Michael C. Thrun, Dipl.-Phys., successfully defended his Ph.D. in 2017 at the Philipps University of Marburg. Thrun’s advisor was the Chair of Neuroinformatics, Prof. Dr. rer. nat. Alfred G. H. Ultsch.

Computer science. --- Data structures (Computer science). --- Pattern recognition. --- Computer Science. --- Pattern Recognition. --- Data Structures. --- Design perception --- Pattern recognition --- Form perception --- Perception --- Figure-ground perception --- Information structures (Computer science) --- Structures, Data (Computer science) --- Structures, Information (Computer science) --- Electronic data processing --- File organization (Computer science) --- Abstract data types (Computer science) --- Informatics --- Science --- Optical pattern recognition. --- Data structures (Computer scienc. --- Optical data processing --- Pattern perception --- Perceptrons --- Visual discrimination --- Data structures (Computer science) --- Cluster Analysis --- Dimensionality Reduction --- Swarm Intelligence --- Visualization --- Unsupervised Machine Learning --- Data Science --- Knowledge Discovery --- 3D Printing --- Self-Organization --- Emergence --- Game Theory --- Advanced Analytics --- High-Dimensional Data --- Multivariate Data --- Analysis of Structured Data

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The last few years have seen a great increase in the amount of data available to scientists. Datasets with millions of objects and hundreds, if not thousands of measurements are now commonplace in many disciplines. However, many of the computational techniques used to analyse this data cannot cope with such large datasets. Therefore, strategies need to be employed as a pre-processing step to reduce the number of objects, or measurements, whilst retaining important information inherent to the data. Spectral dimensionality reduction is one such family of methods that has proven to be an indispensable tool in the data processing pipeline. In recent years the area has gained much attention thanks to the development of nonlinear spectral dimensionality reduction methods, often referred to as manifold learning algorithms. Numerous algorithms and improvements have been proposed for the purpose of performing spectral dimensionality reduction, yet there is still no gold standard technique. Those wishing to use spectral dimensionality reduction without prior knowledge of the field will immediately be confronted with questions that need answering: What parameter values to use? How many dimensions should the data be embedded into? How are new data points incorporated? What about large-scale data? For many, a search of the literature to find answers to these questions is impractical, as such, there is a need for a concise discussion into the problems themselves, how they affect spectral dimensionality reduction, and how these problems can be overcome. This book provides a survey and reference aimed at advanced undergraduate and postgraduate students as well as researchers, scientists, and engineers in a wide range of disciplines. Dimensionality reduction has proven useful in a wide range of problem domains and so this book will be applicable to anyone with a solid grounding in statistics and computer science seeking to apply spectral dimensionality to their work.

Database management. --- Dimension reduction (Statistics) --- Dimensional analysis. --- Dimensionality reduction (Statistics) --- Reduction, Dimension (Statistics) --- Reduction, Dimensionality (Statistics) --- Computer science. --- Data structures (Computer science). --- Algorithms. --- Artificial intelligence. --- Image processing. --- Computer Science. --- Artificial Intelligence (incl. Robotics). --- Data Structures. --- Algorithm Analysis and Problem Complexity. --- Image Processing and Computer Vision. --- Statistics --- Physical measurements --- Data structures (Computer scienc. --- Computer software. --- Computer vision. --- Artificial Intelligence. --- Machine vision --- Vision, Computer --- Artificial intelligence --- Image processing --- Pattern recognition systems --- Software, Computer --- Computer systems --- AI (Artificial intelligence) --- Artificial thinking --- Electronic brains --- Intellectronics --- Intelligence, Artificial --- Intelligent machines --- Machine intelligence --- Thinking, Artificial --- Bionics --- Cognitive science --- Digital computer simulation --- Electronic data processing --- Logic machines --- Machine theory --- Self-organizing systems --- Simulation methods --- Fifth generation computers --- Neural computers --- Data structures (Computer science) --- Information structures (Computer science) --- Structures, Data (Computer science) --- Structures, Information (Computer science) --- File organization (Computer science) --- Abstract data types (Computer science) --- Optical data processing. --- Optical computing --- Visual data processing --- Integrated optics --- Photonics --- Computers --- Algorism --- Algebra --- Arithmetic --- Optical equipment --- Foundations --- Computer science --- Mathematics.

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This open access book introduces and explains machine learning (ML) algorithms and techniques developed for statistical inferences on a complex process or system and their applications to simulations of chemically reacting turbulent flows. These two fields, ML and turbulent combustion, have large body of work and knowledge on their own, and this book brings them together and explain the complexities and challenges involved in applying ML techniques to simulate and study reacting flows. This is important as to the world’s total primary energy supply (TPES), since more than 90% of this supply is through combustion technologies and the non-negligible effects of combustion on environment. Although alternative technologies based on renewable energies are coming up, their shares for the TPES is are less than 5% currently and one needs a complete paradigm shift to replace combustion sources. Whether this is practical or not is entirely a different question, and an answer to this question depends on the respondent. However, a pragmatic analysis suggests that the combustion share to TPES is likely to be more than 70% even by 2070. Hence, it will be prudent to take advantage of ML techniques to improve combustion sciences and technologies so that efficient and “greener” combustion systems that are friendlier to the environment can be designed. The book covers the current state of the art in these two topics and outlines the challenges involved, merits and drawbacks of using ML for turbulent combustion simulations including avenues which can be explored to overcome the challenges. The required mathematical equations and backgrounds are discussed with ample references for readers to find further detail if they wish. This book is unique since there is not any book with similar coverage of topics, ranging from big data analysis and machine learning algorithm to their applications for combustion science and system design for energy generation. .

Fossil fuel technologies --- Engineering thermodynamics --- Machine learning --- Thermodynamics & heat --- Machine Learning --- Combustion Simulations --- Combustion Modelling --- Big Data Analysis --- Dimensionality reduction --- Reduced-order modelling --- Neural Networks --- Turbulent Combustion --- Physics-based modelling --- Data-driven modelling --- Deep learning --- Thermoacoustics and its modelling --- Reactive molecular dynamics --- Simulations of reacting flows --- Cogeneration of electric power and heat. --- Fossil fuels. --- Thermodynamics. --- Heat engineering. --- Heat transfer. --- Mass transfer. --- Machine learning. --- Fossil Fuel. --- Engineering Thermodynamics, Heat and Mass Transfer. --- Machine Learning. --- Chemistry, Physical and theoretical --- Dynamics --- Mechanics --- Physics --- Heat --- Heat-engines --- Quantum theory --- Mass transport (Physics) --- Thermodynamics --- Transport theory --- Heat transfer --- Thermal transfer --- Transmission of heat --- Energy transfer --- Mechanical engineering --- Fossil energy --- Fuel --- Energy minerals --- Combined electric power and heat production --- Electric power and heat cogeneration --- Heat and electric power cogeneration --- Electric power production --- Learning, Machine --- Artificial intelligence --- Machine theory